The VIMOS Public Extragalactic Redshift Survey (VIPERS)

Abstract

Aims. Various galaxy classification schemes have been developed so far to constrain the main physical processes regulating evolution of different galaxy types. In the era of a deluge of astrophysical information and recent progress in machine learning, a new approach to galaxy classification has become imperative. Methods. In this paper, we employ a Fisher Expectation-Maximization (FEM) unsupervised algorithm working in a parameter space of 12 restframe magnitudes and spectroscopic redshift. The model (DBk) and the number of classes (12) were established based on the joint analysis of standard statistical criteria and confirmed by the analysis of the galaxy distribution with respect to a number of classes and their properties. This new approach allows us to classify galaxies based on only their redshifts and ultraviolet to near-infrared (UV-NIR) spectral energy distributions. Results. The FEM unsupervised algorithm has automatically distinguished 12 classes: 11 classes of VIPERS galaxies and an additional class of broad-line active galactic nuclei (AGNs). After a first broad division into blue, green, and red categories, we obtained a further sub-division into: three red, three green, and five blue galaxy classes. The FEM classes follow the galaxy sequence from the earliest to the latest types, which is reflected in their colours (which are constructed from rest-frame magnitudes used in the classification procedure) but also their morphological, physical, and spectroscopic properties (not included in the classification scheme). We demonstrate that the members of each class share similar physical and spectral properties. In particular, we are able to find three different classes of red passive galaxy populations. Thus, we demonstrate the potential of an unsupervised approach to galaxy classification and we retrieve the complexity of galaxy populations at z ∼ 0.7, a task that usual, simpler, colour-based approaches cannot fulfil.

Synergies between low- and intermediate-redshift galaxy populations revealed with unsupervised machine learning

“…52, 114 of these objects (51, 522 galaxies and 592 broad-line active galactic nuclei) have 'secure' (> 99 % confidence) redshifts. This secure-redshift sample was the subject of the Siudek et al (2018b) study, and is the basis of our present VIPERS sample 2 .…”

“…UV photometry was then measured using a Bayesian approach with the * -band profiles of galaxies as priors (Conseil et al 2011), which mitigated the confusion of sources due to their blended UV profiles. UV photometry in at least one of GALEX's two bands (almost always if just one) is available for 52 per cent of galaxies in the Siudek et al (2018b) sample and in our final VIPERS sample (Section 2.2.2).…”

“…We also take VIDEO survey , , , , and NIR photometry for our sample where available (11 per cent of the Siudek et al 2018b sample, 10 per cent of our final VIPERS sample; Section 2.2.2). CFHT -band photometry is available for 91 per cent of galaxies in the Siudek et al (2018b) sample, and for 93 per cent of galaxies in our final VIPERS sample (Section 2.2.2).…”

“…These colours differ from those used by Siudek et al (2018b); they used rest-frame colours defined with reference to the rest-frame -band magnitudes of galaxies ( − , − , etc. ), also with the aim of removing the influence of galaxy intrinsic brightnesses on their clustering outcomes.…”

“…photometric, spectroscopic, morphological), are encoded in just their ultraviolet-through-infrared colours. Our sample of galaxies at low redshift ( ∼ 0.06) is drawn from the second edition of the GALEX-SDSS-WISE Legacy Catalogue (GSWLC-2; Salim et al 2018), and our sample of galaxies at intermediate redshift ( ∼ 0.65) is based on the VIPERS sample of Siudek et al (2018b). We prepare our samples carefully to ensure a fair comparison of galaxies from different cosmic epochs and different surveys, and to mitigate methodological influences on the clustering outcomes.…”